Significant interest exists in the automotive industry for systems that detect objects and utilize the object-detection information in safety, situational awareness, and/or navigational systems. These systems typically detect the presence of potential objects, determine their speed and trajectory, and in the case of safety systems assess their collision threat. Prior art collision avoidance systems are configured to detect such potential objects, however, are limited to several constraints, such as the size of the object, the distance of the object from the camera and the field of view.
One method of detecting objects of potential threats can be found in US 2004/0252863A1, wherein one or more patches are computed in a regular, contiguous rectilinear grid, referred to as “tessellation.” Since each patch is an abstraction of (typically) a few hundred data points, this greatly reduces the number of data points that must be processed. Additionally, the regularity of the patch tessellation grid allows for fast hardware implementations (e.g., by FPGA or ASIC) of the initial, computationally-intensive patch-fitting to the 3D depth points. This approach aggregates patches together using simplified rules, considering patches to be connected if they were within fixed height, width and depth tolerances. This approach was acceptable when considering only large objects positioned proximal to (e.g. within 10 meters) the camera which might cause an imminent collision, wherein an aggregated group of patches directly in front of the cameras would always be considered a single object (in particular, a vehicle). However, this approach is limited in the detection of multiple objects at further distances, since it has no way of effectively representing them.
As disclosed in US 2004/0252863A1, threat object detection can be performed in connection with identifying an imminent collision with a threat vehicle. However, this approach is also limited to vehicles or large objects at a short range (i.e. within 10 meters or less) with a 50° field-of-view (FOV), thus detecting for only those objects with a very high FOV and within a limited range.
Therefore, there is a need in the art for new and improved techniques for detecting one or more objects (e.g. threats or potential threats) that are smaller in size and are located at extensive distances from a camera having a limited FOV.